Contextual self-checkout based verification

ABSTRACT

A transaction is detected on a terminal. A context for an association of items, bags, shelving, carts, and individuals present during the transaction is maintained from images captured during the transaction. Rules are processed to determine actions to process during the transaction and/or after the transaction based on reported transaction details, the context, and analytics derived from the images for the context. In an embodiment, at least one rule identifies an action that overrides an attendant intervention at the terminal, which would have been issued for a detected weight discrepancy by a scale of the terminal.

BACKGROUND

Increasingly, consumers are using Self-Service Terminals (SSTs) toperform self-checkouts at retail outlets. This allows the retailer tomore effectively utilize staff for other purposes besides checkingconsumers out of the stores. Consumers enjoy the convenience ofself-checkouts and the reduced amount of time that it takes to checkoutby avoiding long cashier-assisted checkout queues.

To avoid consumer theft, which is extremely costly to low marginretailers (such as grocery stores), the retailers have a variety ofsecurity measures deployed at the SSTs. The retailers also retain atleast one assistant to assist consumers when needed duringself-checkouts.

However, there are far too many situations where false positive securityevents are raised by the SSTs that require attendant intervention, whichdetract from a consumer's expectation of convenience and reducedcheckout time.

For example, a consumer's child may be placing items on the bag scaleand removing items. A particularly large item may not be fully situatedon the produce scale (half of the item is resting on the counter so asto not detect its full weight). A consumer may remove bags from one sideof the counter or from the cart and place on the bagging scale in aneffort to organize the bags before leaving. A consumer may bere-shuffling items from one bag to a different bag. A consumer may holdback some scanned items to scan another heavier item, which the consumerthen puts in the bag followed by the held back items for purposes oforganizing the bag.

Another issue that is problematic for false positiveattendant-assistance calls is associated with low weight items.Typically, as an item code is scanned or inputted into the SST, theconsumer is instructed to place the item in the bag and the bag/bag racksits on a bag scale. The scanned item includes an item code and the itemcode is linked to a known average weight for the item. The bag scalealso has a tolerance below which the scale may not return any weight.The average weight of any given item is dynamically averaged, such thatconsumers that scan one low weight item and stick multiple low weightitems in the bag can change the average weight of that item for a nextconsumer that buys that item, which can result in attendant calls foreach subsequent consumer buying that item.

SUMMARY

In various embodiments, methods and a system for contextualself-checkout based verification are presented.

According to an embodiment, a method for contextual self-checkout basedverification is presented. Specifically, and in one embodiment, atransaction is monitored from images captured of a checkout area. Acontext is derived for the transaction from the images. The context iscompared to a transaction state being processed independently for thetransaction at a transaction terminal within the checkout area. Anaction is identified from a rule and processed based on the comparisonof the transaction state to the context.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of a system for contextual self-checkout basedverification, according to an example embodiment.

FIG. 2 is a diagram of a method for contextual self-checkout basedverification, according to an example embodiment.

FIG. 3 is a diagram of another method for contextual self-checkout basedverification, according to an example embodiment.

FIG. 4 is a diagram of terminal having contextual self-checkout basedverification, according to an example embodiment.

DETAILED DESCRIPTION

FIG. 1 is a diagram of a system 100 for contextual self-checkout basedverification, according to an example embodiment. It is to be noted thatthe components are shown schematically in greatly simplified form, withonly those components relevant to understanding of the embodiments beingillustrated.

Furthermore, the various components (that are identified in the FIG. 1 )are illustrated and the arrangement of the components is presented forpurposes of illustration only. It is to be noted that other arrangementswith more or less components are possible without departing from theteachings of contextual self-checkout based verification presentedherein and below.

The system 100 includes a SST station 110. The SST station 120 includesa SST 120 having a transaction manager 121, a produce scale 122, and abag scale 123. The system 100 further includes a contextual manager 130,one or more cameras 140, a security/alert system 150, andweight-learning database (WLDB) 160.

The transaction manager 121 is implemented as executable instructionsthat reside in a non-transitory computer-readable storage medium and areexecuted by a processor of the SST 120.

The contextual manager 130 is implemented as executable instructionsthat reside in a non-transitory computer-readable storage medium and areexecuted by a processor of a server. In an embodiment, the contextualmanager 130 resides on and is executed by a processor of the SST 120.

The security/alert system 150 includes one or more services that areimplemented as executable instructions and executed from anon-transitory computer-readable storage medium by one or moreprocessors of one or more servers.

The WLDB 160 includes one or more services that are implemented asexecutable instructions and executed from a non-transitorycomputer-readable storage medium by one or more processors of one ormore servers.

The SST station 110 includes a variety of structures and devices forwhich a consumer (may also be referred to herein as “user,” “customer,”or “operator of the SST 120) performs a self-checkout for items beingpurchased at a retailer. It is noted that the SST 120 may include avariety of other peripheral-based devices, such as a touchscreendisplay, a barcode scanner, a camera, and integrated scanner/camera, acurrency acceptor, a currency dispenser, a card reader, and a receiptprinter.

As an item in a transaction is inputted (through item barcode scanningor touchscreen entry of an item code), the transaction manager 121obtains the item information including item pricing and expected itemaverage weight from the WLDB 160.

Weights for produce are weighed by the produce scale 122 and as eachitem is placed in bags, a weight of the item is provided by the bagscale 123. The transaction manager 121 manages the weights against whatis expected for the item from the average item weights provided from theWLDB 160 within configured tolerances.

Concurrently, images of the consumer operating the SST 120 are capturedby the cameras 140. If discrepancies in item weights are noted by thetransaction manager for weights provided by either the produce scale 122or the bag scale 123, the transaction manager 121 raises an alert; butrather than alerting an attendant to assist the consumer with thetransaction, the transaction manager 121 provides the alert to thecontextual manager 130.

The contextual manager 130 tracks item movement within the SST stationas the transaction progresses with the transaction, such that thelocation of any given item is known by the contextual manager 130 whenthe discrepancy alert is raised by the transaction manager 121. Thecontextual manager 130 uses metadata templates for rapidly identifyingspecific items based on the items' known pixel attributes (shape, color,edges, dimensions, etc.). Moreover, the location of the bag scale 123,the produce scale 122, a consumer cart, if any, existing bags in use bythe consumer, and any individuals with the consumer at the SST station110 are identified from the images. Such that at any given point in timethe contextual manager 130 knows where an item is located (such as in aparticular bag, in a bag that is located in the cart, on the bag rack,on top of an SST station counter, on the floor, being held by anotherindividual other than the consumer, etc.).

The context manager 130 can provide a context with respect to each itemthat is being processed by the transaction manager 121. It is noted thatif an image shows the item being placed in a bag and subsequent imageshows the bag being placed in the cart, then the last-known or expectedlocation for the item is in a bag within the cart. That is, any givenimage may have an obscured or no image detail at all for a given itembut the last known location is known and is tracked by the contextualmanager 130, such that when the transaction manager 121 raises an alertevent, the context of the item that is associated with the item isknown.

The alert raised by the transaction manager 121 can then be clearedbased on the resolved context by the contextual manager 130. This avoidsthe transaction manager 121 suspending the transaction and requesting anattendant's assistance (avoids a false positive).

For example, a consumer may have removed an item from a bag on the bagscale when scanning a next item and placed the removed item in a bagthat is already in the cart. The transaction manager 121 raises an alertbecause the bag scale 123 was expecting an increase in weight thataccounted for both removed item and then newly scanned item. Thecontextual manager 130 identifies the removed item as being in the cartand instructs the transaction manager to clear the alert and continuewith transaction processing.

In an embodiment, the contextual manager 130 may identify the removeditem to the transaction manager 121, and the transaction manager removesthe recorded weight for the removed item from what it expects to seefrom the bag scale 123 when checking for the weight with the newlyscanned item added. Thus, the contextual manager 130 may just instructthe transaction manager 121 to override the alert or may provide thetransaction manager 121 with enough detail to correct the weights beingchecked to account for a given situation taken by the consumer, suchthat weights can continue to be monitored with corrected weightinformation.

In an embodiment, the contextual manager 130 may proactively identify apotential situation during the transaction to the transaction manager121, such as when a produce item being weighed by the produce scale 122was partially resting on the adjacent shelf surface and was notcompletely placed on the produce scale surface. The transaction manager121 receives the notifications and displays a message to the consumer toreweigh the produce item and ensure the item is centered on the producescale 122 without resting on the adjacent surface.

The contextual manager 130 also provides video analytics for theactivity depicted in the images, such as a total of 5 items weretracked, all 5 items are in a bag, in possession of the consumer, in aconsumer bag, or in a cart of the consumer. If the transaction manager121 is showing 4 items scanned and the images depict the consumer movingin the direction of an exit, the contextual manager 130 can send anotification to the security/alert system 150, turn on security lights,instruct the transaction manager to illuminate a sound or light, etc.

The video analytics can also be used to detect when the consumer leavesthe cart and all the items at the SST station 110 and exits the storewithout paying. Here, there can be a variety of reasons why this mayhave occurred, such as the consumer forgot they did not bring a paymentmeans with them to pay for the items and is simply electing to leave thestore. In such a situation, no security alert is raised, but thecontextual manager 130 may send a notification to other store personnelto come and restock the items present at the SST station 110. Anothercase may be when the consumer leaves the items to go to a service deskfor assistance. The video analytics can identify that the consumer isleaving the SST station 110 but not with any of the items, such that nosecurity situation is needed.

The video analytics can also provide the context when a consumer leavesbehind an item or a consumer bag (purse) at the SST station 110. Thenumber of items purchased based on the transaction manager's processingindicates more than what the consumer is leaving the store with. Here,an alert can be raised on the SST 120 or at an egress point, to remindthe consumer or to get the attention of the consumer to return for themissed item or bag.

In this way, the contextual manager 130 can compare the video analyticsfor the transaction as determined from the images against transactionitem details produced by the transaction manager 121 for thetransaction. Rules are used to determine the processing actions of thecontextual manager 130 based on the comparison, such as: 1) a rule thatis the total items detected in the images exceed the total items of thetransaction raise a security alert to the security/alert system 150; 2)a rule that if the total items in the images is less than the transacteditem raise alerts to get the attention of staff and/or the consumerbefore exiting the store; 3) a rule that if the consumer leaves all theitems at the station 110 cancel the transaction and alert personnel torestock the items when the consumer is detected in the images as leavingthe store; 4) a rule that if the consumer leaves all the items at thestation 110 temporarily suspend the transaction when the consumer isdetected in the images as heading towards a service desk; 5) a rule thatis a personal item of the consumer, such as a purse is left behind andthe consumer is headed towards an exit raise alerts to personnel and tocapture the attention of the consumer to return for the purse.

In fact, the current state of the transaction as provided by thetransaction manager 121 during the transaction and the existing contextas currently resolved by the contextual manager 130 for the items can beassociated with rules that determine what action, if any, the contextualmanager 130 processes or instructs the transaction manager 121 to take.

The contextual manager 130 can also be processed to provide context tohandle practically difficult low weight items placed on the bag scale.As sated before, each item has an average weight that is recalculatedeach time an item is purchased and weighed. If a consumer were to scan alow weight item having a low average weight, but then eitherintentionally or unintentionally places multiple ones of the items in abag on the bag scale 123, the combined total weight of the multipleitems may be within a configured tolerance, such that no alert is raised(although the contextual manager 130 would capture this detail and alertthe transaction manager 121 or raise a secure alert base on the videoanalytics for the transaction as a whole (item count mismatch)). Thetransaction manager 121 reports the combined weight for the multipleitems as a new weight to record to the WLDB 160 when calculating therunning weight average for the item. This means that the next consumerthat transacts with the same item is going to trigger an assistanceintervention by the transaction manager 121 because the average itemweight was increased by the multiple items (identified as one item bythe previous consumer). This is a particularly problematic situation forsubsequent consumers buying the same item and means that a falsepositive intervention alert will likely be raised for each of thesubsequent consumers buying the same item.

Here, the contextual manager 130 can force the transaction manager 121to override the false positive. However, as the subsequent items areweighed, their recorded weights can be added back to the WLDB 160, suchthat the average weight for the item self-corrects over time as more andmore of those items are purchased. Furthermore, the WLDB 160 can beinstructed to relearn and start over with calculating the average weightfor the item, so that the self-correction happens more rapidly andsooner than waiting on the average to be decreased over time as more andmore of the same items are purchased by consumers.

The contextual manager 130 resolves a context of a transaction bytracking item movements within the SST station 110 as soon as a consumerapproaches the SST station 110 and, in some cases, before a transactionis initiated by the consumer on the SST120. The context defines itemlocations, bags in use and their locations, cart in sue and itslocation, known locations for the produce scale 122 and the bag scale123, known shelving locations for the SST station 110,consumer-possessed items that are detectable for the images (such aspurse or personal bags) and their locations, and individuals presentwith the consumer at the SST station 110. The contextual manager 130also provides video analytics for the items (such as total number ofdetected items, direction of movement of the consumer away from the SSTstation 110, etc.). The context and the video analytics are used duringand after the transaction to override false positives requiringattendant intervention and to identify security situations or situationsthat are not security related but require attention.

A rules-based approach allows the actions of the contextual manager 130to be user-defined based on context and the video analytics and in viewof current transaction state provided by the transaction manager 121.

By supplementing a transaction with the physical context of the SSTstation 110 before, during, and after a transaction, false attendantinterventions alerts can be substantially reduced and more intelligentsecurity decisions can be made. Additionally, low weight items can behandled while resolving the average expected weights of such items in amore dynamic and real-time fashion.

In an embodiment, the context defines an association of each item: tothe consumer, to someone with the consumer, a bag, a shelf, a personalbag of the consumer, and a cart. Furthermore, the context definesassociations of each bag, when present to: a consumer, someone with theconsumer, a shelf, a personal bag of the consumer, and a cart. Theassociations may be viewed as the location of the items, the bags, andthe cart relative to a SST station shelf, the consumer, or someone withthe consumer.

In an embodiment, the contextual manager 130 includes a trained machinelearning algorithm that tracks the items, carts, bags, consumer, andindividuals with the consumer from images provided by the contextualmanager 130. The input is an image taken at the SST station 110 and theoutput is a current context for the transaction being processed at theSST station 110.

In an embodiment, the SST station 110 is configured as acashier-assisted checkout station where a cashier is checkout out aconsumer with the SST 120 operated in a cashier-assisted mode ofoperation.

In an embodiment, the SST station 110 is configured for self-checkoutswhere the consumer is self-checking out with items.

These and other embodiments will now be discussed with reference to theFIGS. 2-4 .

FIG. 2 is a diagram of a method 200 for contextual self-checkout basedverification, according to an example embodiment. The software module(s)that implements the method 200 is referred to as a “transactioncontextual manager.” The transaction contextual manager is implementedas executable instructions programmed and residing within memory and/ora non-transitory computer-readable (processor-readable) storage mediumand executed by one or more processors of a device. The processor(s) ofthe device that executes the transaction contextual manager arespecifically configured and programmed to process the transactioncontextual manager. The transaction contextual manager may have accessto one or more network connections during its processing. The networkconnections can be wired, wireless, or a combination of wired andwireless.

In an embodiment, the device that executes the transaction contextualmanager is a server

In an embodiment, the device that executes the transaction contextualmanager is the SST 120. In an embodiment, the SST 120 is executed in aconsumer-self checkout mode of operation. In an embodiment, the SST 120is operated in a cashier-assisted mode of operation.

In an embodiment, the transaction contextual manager some or all of: thecontextual manager 130 and/or the transaction manager 121.

At 210, the transaction contextual manager monitors a transaction fromimages of a checkout area. The images can be captured from a pluralityof cameras 140 situated overhead and around the checkout area. Thecheckout area includes a SST.

According to an embodiment, at 211, the transaction contextual managertracks items represented in the images. The identity of particular itemsdo not have to be identified; rather, the transaction contextual managercan label unique items separately. In this manner, the transactionmanager 121 does not have to provide any item identifiers and thetransaction contextual manager does not have to identify an item codefrom the images.

In an embodiment of 211 and at 212, the transaction contextual managertracks bags, shelves of the checkout area, and any cart (if present)within the images. The shelves can be used as a reference point in theimages, since their locations are known or predefined.

In an embodiment of 212 and at 213, the transaction contextual managertracks a consumer represented within the images. Again, the identity ofthe consumer does not have to be known and attributes associated withpeople can be determined from the images and used for tracking theconsumer from image to image (frame to frame).

At 220, the transaction contextual manager derives a context for thetransaction from the images. The context was discussed above with theFIG. 1 .

In an embodiment of 213 and 220, at 221, the transaction contextualmanager resolves proximity associations between the items, the bags, theshelves, the cart, and the consumer. A proximity association infers arelationship (based on computed distances or overlapping locationswithin the image) that the item has with the bags, shelves, cart, andthe consumer.

At 230, the transaction contextual manager compares the context to atransaction state associated with a transaction that is being processedon the transaction terminal within the checkout area. The transaction isbeing independently processed by the transaction terminal. That is, theprogression of the processing of the transaction on the transactionterminal is not determined by the processing being performed by thetransaction contextual manager. The transaction is independently beingfollowed by the transaction contextual manager.

In an embodiment of 221 and 230, at 231, the transaction contextualmanager receives the transaction state as an attendant interventioninterrupt generated by the transaction manager that executes on thetransaction terminal for an item weight mismatch recorded on a bagscale.

In an embodiment of 231 and at 232, the transaction contextual manageridentifies a particular proximity association indicating that the itemweight mismatch if for a particular item that was removed from the bagscale and was placed in the cart by the consumer.

In an embodiment of 231 and at 233, the transaction contextual manageridentifies a particular proximity association indicating that the itemweight mismatch is for a particular item that was placed directly in thecart bypassing the bags or placed directly on one of the shelvesbypassing the bags of the bag scale.

At 240, the transaction contextual manager processes an actionidentified from a rule based on 230. That is, user-defined rules can beidentified for any given context detected and the rules determine anautomated action that the transaction contextual manager processes.

In an embodiment, at 241, the transaction contextual manager instructsthe transaction terminal to override an attendant intervention interruptrequest based on the rule.

In an embodiment, at 242, the transaction contextual manager comparesitem totals reported by the transaction terminal with image item totalsidentified within the context. That is, video or image analytics arederived from the context and include image item totals.

In an embodiment of 242 and at 243, the transaction contextual managersends a security alert to a security system when the image item totalsexceed the item totals and when the consumer is tracked within theimages as moving towards an exit with the items associated with thetransaction. This may be an indication of theft or may be an honestmistake of the consumer, but the transaction contextual managerprocesses a rule associated with the context and the analytics to sendan alert to the security system.

FIG. 3 is a diagram of another method 300 for contextual self-checkoutbased verification, according to an example embodiment. The softwaremodule(s) that implements the method 300 is referred to as a“transaction context resolver.” The transaction context resolver isimplemented as executable instructions programmed and residing withinmemory and/or a non-transitory computer-readable (processor-readable)storage medium and executed by one or more processors of a device. Theprocessors that execute the transaction context resolver arespecifically configured and programmed to process the transactioncontext resolver. The transaction context resolver may have access toone or more network connections during its processing. The networkconnections can be wired, wireless, or a combination of wired andwireless.

In an embodiment, the device that executes the transaction contextresolver is a server or a cloud processing environment.

In an embodiment, the device that executes the transaction contextresolver is the SST 120. In an embodiment, the SST 120 is operated in aconsumer self-checkout mode of operation by a consumer performing aself-checkout transaction. In an embodiment, the SST 120 is operated ina cashier-assisted mode of operation by a cashier assisting a consumerwith a checkout transaction.

In an embodiment, the transaction context resolver is all or somecombination of: the transaction manager 121, the contextual manager 130,and/or the method 200.

At 310, the transaction context resolver captures images of aself-checkout (SCO) area having a SST, a bag scale, a produce scale, andshelves or countertops for resting items or bags.

At 320, the transaction context resolver identifies from the images aconsumer in the SCO area. This identification can occur before anyactual transaction is initiated on the SST by the consumer, such thattracking can begin before the transaction actually is initiated.

At 330, the transaction context resolver tracks items being processed atthe SST from the images during a transaction. As stated before, thistracking may have begun at 320 once the consumer initiates thetransaction at 330, the transaction context resolver actively tracksspecific items being processed from the loading area shelves, the cart,or a consumer bag with the transaction terminal.

At 340, the transaction context resolver determines a context betweeneach item with respect to one or more of: the consumer, a cart (ifpresent), the bag scale, bags, the produce scale, and the shelves(countertops).

In an embodiment, at 341, the transaction context resolver resolves aproximity distance within the images for each item to the consumer, thebags, the cart, the bag scale, the produce scale, and the shelves(countertops). Again, this proximity distance is an inferredrelationship that any given item has with the above-stated items, whichimplies a location that the item is currently at in any given imageframe.

In an embodiment of 341 and at 342, the transaction context resolverinfers a specific item is in a particular bag and has no proximitydistance when a last image processed for the specific item indicatedthat the specific item was inserted into the particular bag.

In an embodiment of 342 and at 343, the transaction context resolverinfers the specific item is in the cart when a last processed image forthe particular bag indicated that he bag was inserted into the cart.

At 350, the transaction context resolver receives an attendantintervention event (interrupt) raised by the SST for a particular itembased on an unexpected weight provided by the bag scale or the producescale.

At 360, the transaction context resolver uses the context to instructthe SST to override or ignore the event and continue with processing anext item of the transaction (assuming there is a next item and theparticular item was not the last item of the transaction).

In an embodiment, at 361, the transaction context resolver instructs theSST to remove the unexpected weight for the particular item fromconsideration of a next weight provided by the bag scale or the producescale. In this embodiment, the transaction manager 121 is dynamicallyadjusted to remove the unexpected weight from any next item that isbeing weighed.

In an embodiment, at 370, the transaction context resolver determinesitem totals for the images when the transaction concludes and thetransaction context resolver then compares the item totals calculatedwith transaction totals provided by the SST.

In an embodiment, at 380, the transaction context resolver performs avariety of additional processing including one or more of: sending analert to an attendant to restock items left at the SST when the consumerleft the SST without taking the items of concluding the transaction(here the transaction is cleared or canceled from the SST); sending amessage to the SST to hold the transaction in abeyance in a suspendedstate when the consumer is detected at or heading in the direction of aservice desk with the items remaining at the SST; and/or send anotification to an alert system when the consumer is headed towards anexit without having at least one of the items that were purchased andpaid for by the consumer.

FIG. 4 is a diagram of a system 400 for contextual self-checkout basedverification, according to an example embodiment. The system 400includes a variety of hardware components and software components. Thesoftware components of the system 400 are programmed and reside withinmemory and/or a non-transitory computer-readable medium and execute onone or more processors of the system 400. The system 400 may communicateover one or more networks, which can be wired, wireless, or acombination of wired and wireless.

In an embodiment, the system 400 implements, inter alia, the processingdescribed above with respect to all of or some combination of: thesystem 100, the method 200, and/or the method 300.

The system 400 includes at least one processor 401, memory 402, andnon-transitory computer-readable storage media 403. The non-transitorycomputer-readable storage media includes executable instructionsrepresenting a context manager 404.

The executable instructions when executed from the non-transitorycomputer-readable storage medium 403 cause the processor 402 to performthe processing of the context manager 404.

The context manager 405 is configured to: independently monitor atransaction area through images taken of the transaction areaindependent of a transaction terminal that processes a transaction;clear attendant intervention alerts when a context from the images and atransaction state at a transaction terminal of the transaction areaindicates the attendant intervention alerts are unnecessary; maintainanalytics for items processed during a transaction; compare theanalytics to transaction totals when the transaction concludes; andprocess custom-defined actions based on rules associated with thecontext and the analytics.

In an embodiment, the context manager 405 is all or some combination of:the contextual manager 130, the method 200, and/or the method 300.

In an embodiment the transaction area is the SST station 110. In anembodiment, the context manager 405 interacts with the transactionmanager 121 that executes on the SST 120.

It is to be noted that although the various examples presented werewithin the context of online middleware food service providers, otherembodiments of the invention are not so limited, such that any retailermiddleware service that sells products other than food can benefit fromthe teachings presented herein and above.

It should be appreciated that where software is described in aparticular form (such as a component or module) this is merely to aidunderstanding and is not intended to limit how software that implementsthose functions may be architected or structured. For example, modulesare illustrated as separate modules, but may be implemented ashomogenous code, as individual components, some, but not all of thesemodules may be combined, or the functions may be implemented in softwarestructured in any other convenient manner.

Furthermore, although the software modules are illustrated as executingon one piece of hardware, the software may be distributed over multipleprocessors or in any other convenient manner.

The above description is illustrative, and not restrictive. Many otherembodiments will be apparent to those of skill in the art upon reviewingthe above description. The scope of embodiments should therefore bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

In the foregoing description of the embodiments, various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting that the claimed embodiments have more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Description of the Embodiments, with each claimstanding on its own as a separate exemplary embodiment.

The invention claimed is:
 1. A method, comprising: providing executableinstructions to a processor of a server causing the processor to executethe executable instructions and perform operations comprising:monitoring a transaction from images captured of a checkout area;maintaining a context for items of the transaction during thetransaction with respect to bags, shelves of the checkout area, a cart,and a produce scale of a terminal from the monitoring of the imagesimages, wherein maintaining further includes maintaining the context asitem locations for the items within the checkout area, bag locations forthe bags within the checkout area, cart location for the cart within thecheckout area, produce scale location for the produce scale within thecheckout area, bag scale location for a bag scale within the checkoutarea, shelf locations for the shelves within the checkout area,consumer-possessed items associated with a consumer and detectablewithin the checkout area, and any individuals present with the consumerin the checkout area, wherein maintaining further includes maintaininganalytics with the context, wherein the analytics include a total numberof items detected and direction of movement of the consumer away fromthe terminal; comparing the context to a transaction state beingprocessed independently for the transaction at the terminal within thecheckout area; and processing an action identified from a rule based onthe comparing of the context to the transaction state.
 2. The method ofclaim 1, wherein monitoring further includes tracking each of the itemsrepresented within the images.
 3. The method of claim 2, whereintracking further includes tracking the bags, the shelves of the checkoutarea, and the cart represented within the images with respect to theitems.
 4. The method of claim 3, wherein tracking further includestracking the consumer represented within the images with respect to theitems, the bags, the shelves of the checkout area, and the cart.
 5. Themethod of claim 4, wherein maintaining further includes resolvingproximity associations between the items, the bags, the shelves, thecart, and the consumer within the context.
 6. The method of claim 5,wherein comparing further includes receiving the transaction state as anattendant intervention sent from the terminal for an item weightmismatch recorded on the bag scale.
 7. The method of claim 6, whereinreceiving further includes identifying a particular proximityassociation indicating that the item weight mismatch is for a particularitem that was removed from the bag scale and was placed in the cart. 8.The method of claim 6, wherein receiving further includes identifying aparticular proximity association indicating that the item weightmismatch is for a particular item that was placed directly in the cartbypassing the bags or placed on one of the shelves.
 9. The method ofclaim 1, wherein processing further includes instructing the terminal tooverride an attendant intervention request based on the rule.
 10. Themethod of claim 1 further comprising, comparing item totals reported inthe transaction state with image item totals identified in the context.11. The method of claim 10 further comprising sending a security alertto a security system when the image item totals exceed the item totalswhen the consumer being tracked within the images is detected as movingtowards an exit with the items associated with the transaction.
 12. Amethod, comprising: providing executable instructions to a processor ofa server causing the processor to execute the executable instructionsand perform operations comprising: capturing images of a Self-Checkout(SCO) area having a Self-Service Terminal (SST), a bag scale, a producescale, and shelves; identifying from the images a consumer in the SCOarea; tracking items being processed at the SST from the images during atransaction; maintaining a context from the images between each itemwith respect to: the consumer, bags of the consumer, a cart of theconsumer, the bag scale, the produce scale, and the shelves, whereinmaintaining further includes maintaining the context as item locationsfor the items within the SCO area, bag locations for bags within the SCOarea, cart location for a cart within the SCO area, produce scalelocation for the produce scale within the SCO area, bag scale locationfor the bag scale within the SCO area, shelf locations for the shelveswithin the SCO area, consumer-possessed items detectable within the SCOarea, and any individuals present with the consumer in the SCO area,wherein maintaining further includes maintaining analytics with thecontext, wherein the analytics include a total number of items detectedand direction of movement of the consumer away from the terminal;receiving an attendant intervention event raised by the SST for aparticular item based on an unexpected weight provided by the bag scaleor the produce scale; and using the context to instruct the SST tooverride the attendant intervention event and continue with a next itemof the transaction.
 13. The method of claim 12, wherein maintainingfurther includes resolving a proximity distance within the images foreach item to the consumer, the bags, the cart, the bag scale, theproduce scale, and the shelves.
 14. The method of claim 13, whereinmaintaining further includes inferring a specific item is in aparticular bag and has no proximity distance to the particular bag whena last image processed for the specific item indicated that the specificitem was inserted into the particular bag.
 15. The method of claim 14,wherein inferring further includes inferring the specific item is in thecart when a last processed image for the particular bag indicated thatthe particular bag was inserted into the cart.
 16. The method of claim12, wherein using further includes instructing the SST to remove theunexpected weight for the particular item from consideration of a nextweight provided by the bag scale or the produce scale.
 17. The method ofclaim 12 further comprising, determining the total number of items fromthe images and an item total processed by the SST when the transactionconcludes.
 18. The method of claim 12 further comprising one or more of:sending an alert to an attendant to restock the items when the imagesindicate the consumer left the SST without paying for the items orconcluding the transaction and canceling the transaction; sending amessage to the SST to hold the transaction in abeyance in a suspendedstate when the images indicate the consumer left the SST and is at aservice desk; and sending a notification to an alert system when theimages indicate the consumer is headed towards an exit without at leastone of the items purchased with the transaction.
 19. A system,comprising: a processor; a memory; a non-transitory computer-readablestorage medium having executable instructions; the executableinstructions when provided to the processor from the non-transitorycomputer-readable storage medium into the memory cause the processor toperform operations comprising: independently monitoring a transactionarea through images taken of the transaction area independent of atransaction terminal that processes a transaction, wherein theindependently monitoring further includes monitoring a consumeridentified in the images performing the transaction at the transactionterminal with respect to a bag scale of the transaction terminal, a cartof the consumer, one or more bags of the consumer, a produce scale, andshelves identified in the transaction area through the images, whereinindependently monitoring further includes maintaining a context for thetransaction as item locations for the items within the transaction area,bag locations for the bags within the transaction area, cart locationfor the cart within the transaction area, produce scale location for theproduce scale within the transaction area, bag scale location for thebag scale within the transaction area, shelf locations for the shelveswithin the transaction area, consumer-possessed items detectable withinthe transaction area, and any individuals present with the consumer inthe checkout area, wherein maintaining further includes maintaininganalytics with the context, wherein the analytics include a total numberof items detected and direction of movement of the consumer away fromthe transaction terminal; clearing attendant intervention alerts whenthe context from the images and a transaction state at a transactionterminal of the transaction area indicates the attendant interventionalerts are unnecessary; maintaining additional analytics for itemsprocessed during a transaction by the transaction terminal; comparingthe additional analytics, the context, and the analytics for the contextto transaction totals when the transaction concludes; and processingcustom-defined actions based on rules associated with the context, theanalytics, the additional analytics, and the transaction totals.
 20. Thesystem of claim 19, the transaction terminal is: a Self-Service Terminal(SST) operated in a self-checkout mode of operation or operated in acashier-assisted mode of operation.